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• Better classification performance compared to discriminative models for small datasets, since the constraints imposed by the generative assumptions needed to build the model work against over-fitting Cons • Poor classification performance compared to discriminative models for medium/large datasets • Requires large datasets to build correct models. • More difficult to train (usually more parameters to tune) This comparative is a generic introduction to the specific architectures proposed in this thesis. In the following sections are presented in detail the new architectures/models proposed in this thesis and how they are applied to the areas listed in Table 6. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 42PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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